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https://www.sciencedirect.com/science/article/pii/S1572312709000574
This paper introduces a Bayesian semi-supervised support vector machine (Semi-BSVM) model for binary classification. Our semi-supervised learning has a distinct advantage over supervised or inductive learning since by design it reduces the problem of overfitting.Cited by: 29
https://www.sciencedirect.com/science/article/abs/pii/S1572312709000574
This paper introduces a Bayesian semi-supervised support vector machine (Semi-BSVM) model for binary classification. Our semi-supervised learning has a distinct advantage over supervised or inductive learning since by design it reduces the problem of overfitting.Cited by: 29
https://www.researchgate.net/publication/257644445_Bayesian_semi-supervised_learning_with_support_vector_machine
This paper introduces a Bayesian semi-supervised support vector machine (Semi-BSVM) model for binary classification. Our semi-supervised learning has a distinct advantage over supervised or ...
https://www.semanticscholar.org/paper/Bayesian-semi-supervised-learning-with-support-Chakraborty/2000f8a7433b0d6486d81accaa700df8feed3bdf
Abstract This paper introduces a Bayesian semi-supervised support vector machine (Semi-BSVM) model for binary classification. Our semi-supervised learning has a distinct advantage over supervised or inductive learning since by design it reduces the problem of overfitting. While a traditional support vector machine (SVM) has the widest margin based on the labeled data only, our semi-supervised ...
https://cs.uwaterloo.ca/~ppoupart/students/Leila-Chianei-mmath-thesis.pdf
Active Learning with Semi-Supervised Support Vector Machines by Leila Chinaei A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science Waterloo, Ontario, Canada, 2007 c Leila Chinaei 2007
https://papers.nips.cc/paper/1582-semi-supervised-support-vector-machines.pdf
In this work we propose a method for semi-supervised support vector machines (S3VM). S3VM are constructed using a mixture of labeled data (the training set) and unlabeled data (the working set). The objective is to assign class labels to the working set such that the "best" support vector machine …
https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md
Jun 13, 2019 · Bayesian Machine Learning. Bayesian Methods for Hackers (using pyMC) Should all Machine Learning be Bayesian? Tutorial on Bayesian Optimisation for Machine Learning. Bayesian Reasoning and Deep Learning, Slides. Bayesian Statistics Made Simple. Kalman & Bayesian Filters in Python. Markov Chain Wikipedia Page. Semi Supervised Learning
https://arxiv.org/pdf/1406.5298.pdf
Table 1 shows classification results. We compare to a broad range of existing solutions in semi-supervised learning, in particular to classification using nearest neighbours (NN), support vector machines on the labelled set (SVM), the transductive SVM (TSVM), and …
https://pomegranate.readthedocs.io/en/latest/semisupervised.html
Semi-Supervised Learning¶ Semi-supervised learning is a branch of machine learning that deals with training sets that are only partially labeled. These types of datasets are common in the world. For example, consider that one may have a few hundred images that …
https://arxiv.org/pdf/1406.5298v1.pdf
of existing solutions in semi-supervised learning, in particular to classification using nearest neigh-bours (NN), support vector machines on the labelled set (SVM), the transductive SVM (TSVM), the Embedded neural networks Weston et al. (2012), and contractive auto-encoders (CAE). Some of
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